Understanding Regression Assumptions (Quantitative Applications in the Social Sciences) 1st Edition

Another great asset if your are using regression as a method of analyzing data for a masters thesis, doctoral dissertation or study. A very well written presentation on the assumptions that precede a regression analytical effort.

I've owned a copy of Berry's Understanding Regression Assumptions for ten years, but I didn't get around to reading it until a few days ago. Better late than never, I suppose, but Berry's text would have been an invaluable adjunct to any of the more complete regression/econometrics texts -- Gujarati, Wooldridge, Wittink -- I've used for teaching multiple regression in years past.It's easy to list the assumptions, explain the consequences of their violation, and provide corrective procedures to assure that OLS regression provides BLUE estimates of slopes. It's a good deal more difficult, however, to identify and explain concrete circumstances that give rise to violation of assumptions in the first place. Sure, mis-specification is easy, at least in principle: variables that should be in the equation are, those that should not be are not, and funtional forms of all relationships are correct. But providing specific examples that illustrate violation of even this most fundamental of assumptions requires a good deal of work on the part of an instructor who wants to do the job right.Futhermore, when we get past the conceptually easier issues and have to provide examples of circumstances that generate, say, heteroscedasticity or serial correlation with cross-sectional data, the task of making these ideas concrete becomes much more demanding. Only a practiced hand who has attended to these issues in purposeful fashion will be able to provided illustrations which will attune students to the conditions that are likely to cause essential assumptions to be violated.Berry's book is replete with examples that make the usual OLS regression assumptions real and easy to remember, rather than leaving them as odd-sounding abstractions that we memorize and take on faith as essential to best use of OLS estimators. I'm retiring at the end of this semester, so maybe better late than never doesn't really apply after all. Still I'm glad I finally got around to reading this fine book.

While equation heavy at times, after reading this book one gains a great deal of understanding as to the weaknesses/boundary conditions for inference of OLS regression. For those with little understanding of the basic mechanics of regression, this is NOT a good starting place, but those with some working knowledge this book is invaluable.

Good but somewhat basic. Good for a newer student in stats, but missing some key topics like polynomials, interactions, etc.

This book is not accessible, not useful and is a waste of money. The book is about 150 pages of statistical babble that has no meaning or relevance to anyone outside of statistics. Understanding regression assumptions is important component of being able to use a statistical software package for data analysis using regression in any meaningful way. Unfortunately this book is written very high-level and does not provide any accessible way to understand the nuances of regression assumptions. Perhaps the author could have spent a few more pages explaining himself and his elaborate functions?

Very good book ! If do you want learn statistics this a usefull tool.